Advances in Privacy Preserving Federated Learning to Realize a Truly Learning Healthcare System
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arXiv
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| Main Authors: | , , , , , |
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| Format: | Preprint |
| Published: |
2024
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| Subjects: | |
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| _version_ | 1866910624390316032 |
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| author | Madduri, Ravi Li, Zilinghan Nandi, Tarak Kim, Kibaek Ryu, Minseok Rodriguez, Alex |
| author_facet | Madduri, Ravi Li, Zilinghan Nandi, Tarak Kim, Kibaek Ryu, Minseok Rodriguez, Alex |
| contents | The concept of a learning healthcare system (LHS) envisions a self-improving network where multimodal data from patient care are continuously analyzed to enhance future healthcare outcomes. However, realizing this vision faces significant challenges in data sharing and privacy protection. Privacy-Preserving Federated Learning (PPFL) is a transformative and promising approach that has the potential to address these challenges by enabling collaborative learning from decentralized data while safeguarding patient privacy. This paper proposes a vision for integrating PPFL into the healthcare ecosystem to achieve a truly LHS as defined by the Institute of Medicine (IOM) Roundtable. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2409_19756 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Advances in Privacy Preserving Federated Learning to Realize a Truly Learning Healthcare System Madduri, Ravi Li, Zilinghan Nandi, Tarak Kim, Kibaek Ryu, Minseok Rodriguez, Alex Cryptography and Security Distributed, Parallel, and Cluster Computing The concept of a learning healthcare system (LHS) envisions a self-improving network where multimodal data from patient care are continuously analyzed to enhance future healthcare outcomes. However, realizing this vision faces significant challenges in data sharing and privacy protection. Privacy-Preserving Federated Learning (PPFL) is a transformative and promising approach that has the potential to address these challenges by enabling collaborative learning from decentralized data while safeguarding patient privacy. This paper proposes a vision for integrating PPFL into the healthcare ecosystem to achieve a truly LHS as defined by the Institute of Medicine (IOM) Roundtable. |
| title | Advances in Privacy Preserving Federated Learning to Realize a Truly Learning Healthcare System |
| topic | Cryptography and Security Distributed, Parallel, and Cluster Computing |
| url | https://arxiv.org/abs/2409.19756 |